






"""simple docstring"""


import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TextClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__SCREAMING_SNAKE_CASE   =   {'LayoutLMv2Config', 'LayoutLMv3Config'}




@is_pipeline_test
class 						__UpperCamelCase     (     unittest.TestCase						):
					lowercase_    :				List[Any]      =  MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
					lowercase_    :				Optional[int]      =  TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

					if model_mapping is not None:
										lowercase_    :				List[str]      =  {config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
					if tf_model_mapping is not None:
										lowercase_    :				List[Any]      =  {
										    config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
										}
					@require_torch
					def        UpperCAmelCase__ (							self							:	Any   )	->       Any:
											lowerCAmelCase   :Optional[int]						=			pipeline(
											    task='text-classification'     , model='hf-internal-testing/tiny-random-distilbert'     , framework='pt'   )

											lowerCAmelCase   :int						=			text_classifier('This is great !'   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'LABEL_0', 'score': 0.5_0_4}]   )

											lowerCAmelCase   :Dict						=			text_classifier('This is great !'     , top_k=2   )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase   )     , [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}]   )

											lowerCAmelCase   :Dict						=			text_classifier(['This is great !', 'This is bad']     , top_k=2   )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase   )     , [
											        [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
											        [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
											    ]     , )

											lowerCAmelCase   :int						=			text_classifier('This is great !'     , top_k=1   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'LABEL_0', 'score': 0.5_0_4}]   )

											# Legacy behavior
											lowerCAmelCase   :List[Any]						=			text_classifier('This is great !'     , return_all_scores=UpperCAmelCase   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'LABEL_0', 'score': 0.5_0_4}]   )

											lowerCAmelCase   :List[str]						=			text_classifier('This is great !'     , return_all_scores=UpperCAmelCase   )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase   )     , [[{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}]]   )

											lowerCAmelCase   :List[Any]						=			text_classifier(['This is great !', 'Something else']     , return_all_scores=UpperCAmelCase   )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase   )     , [
											        [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
											        [{'label': 'LABEL_0', 'score': 0.5_0_4}, {'label': 'LABEL_1', 'score': 0.4_9_6}],
											    ]     , )

											lowerCAmelCase   :Dict						=			text_classifier(['This is great !', 'Something else']     , return_all_scores=UpperCAmelCase   )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase   )     , [
											        {'label': 'LABEL_0', 'score': 0.5_0_4},
											        {'label': 'LABEL_0', 'score': 0.5_0_4},
											    ]     , )
					@require_torch
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Optional[int]:
											import torch

											lowerCAmelCase   :List[str]						=			pipeline(
											    task='text-classification'     , model='hf-internal-testing/tiny-random-distilbert'     , framework='pt'     , device=torch.device('cpu'   )     , )

											lowerCAmelCase   :List[Any]						=			text_classifier('This is great !'   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'LABEL_0', 'score': 0.5_0_4}]   )
					@require_tf
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       Any:
											lowerCAmelCase   :Any						=			pipeline(
											    task='text-classification'     , model='hf-internal-testing/tiny-random-distilbert'     , framework='tf'   )

											lowerCAmelCase   :Optional[int]						=			text_classifier('This is great !'   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'LABEL_0', 'score': 0.5_0_4}]   )
					@slow
					@require_torch
					def        UpperCAmelCase__ (							self							:	int   )	->       Tuple:
											lowerCAmelCase   :Optional[Any]						=			pipeline('text-classification'   )

											lowerCAmelCase   :str						=			text_classifier('This is great !'   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'POSITIVE', 'score': 1.0}]   )
											lowerCAmelCase   :Dict						=			text_classifier('This is bad !'   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'NEGATIVE', 'score': 1.0}]   )
											lowerCAmelCase   :int						=			text_classifier('Birds are a type of animal'   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'POSITIVE', 'score': 0.9_8_8}]   )
					@slow
					@require_tf
					def        UpperCAmelCase__ (							self							:	List[Any]   )	->       Dict:
											lowerCAmelCase   :List[str]						=			pipeline('text-classification'     , framework='tf'   )

											lowerCAmelCase   :Tuple						=			text_classifier('This is great !'   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'POSITIVE', 'score': 1.0}]   )
											lowerCAmelCase   :Any						=			text_classifier('This is bad !'   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'NEGATIVE', 'score': 1.0}]   )
											lowerCAmelCase   :str						=			text_classifier('Birds are a type of animal'   )
											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': 'POSITIVE', 'score': 0.9_8_8}]   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	List[str]     , UpperCAmelCase							:	List[str]   )	->       Any:
											lowerCAmelCase   :Any						=			TextClassificationPipeline(model=UpperCAmelCase     , tokenizer=UpperCAmelCase   )
											return text_classifier, ["HuggingFace is in", "This is another test"]
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	Dict   )	->       Optional[int]:
											lowerCAmelCase   :int						=			text_classifier.model
											# Small inputs because BartTokenizer tiny has maximum position embeddings = 22
											lowerCAmelCase   :List[Any]						=			'HuggingFace is in'
											lowerCAmelCase   :Optional[int]						=			text_classifier(UpperCAmelCase   )

											self.assertEqual(nested_simplify(UpperCAmelCase   )     , [{'label': ANY(UpperCAmelCase   ), 'score': ANY(UpperCAmelCase   )}]   )
											self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()   )

											lowerCAmelCase   :Any						=			['HuggingFace is in ', 'Paris is in France']
											lowerCAmelCase   :str						=			text_classifier(UpperCAmelCase   )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase   )     , [{'label': ANY(UpperCAmelCase   ), 'score': ANY(UpperCAmelCase   )}, {'label': ANY(UpperCAmelCase   ), 'score': ANY(UpperCAmelCase   )}]     , )
											self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()   )
											self.assertTrue(outputs[1]['label'] in model.config.idalabel.values()   )

											# Forcing to get all results with `top_k=None`
											# This is NOT the legacy format
											lowerCAmelCase   :Tuple						=			text_classifier(UpperCAmelCase     , top_k=UpperCAmelCase   )
											lowerCAmelCase   :Dict						=			len(model.config.idalabel.values()   )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase   )     , [[{'label': ANY(UpperCAmelCase   ), 'score': ANY(UpperCAmelCase   )}] * N, [{'label': ANY(UpperCAmelCase   ), 'score': ANY(UpperCAmelCase   )}] * N]     , )

											lowerCAmelCase   :int						=			{'text': 'HuggingFace is in ', 'text_pair': 'Paris is in France'}
											lowerCAmelCase   :Union[str, Any]						=			text_classifier(UpperCAmelCase   )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase   )     , {'label': ANY(UpperCAmelCase   ), 'score': ANY(UpperCAmelCase   )}     , )
											self.assertTrue(outputs['label'] in model.config.idalabel.values()   )

											# This might be used a text pair, but tokenizer + pipe interaction
											# makes it hard to understand that it's not using the pair properly
											# https://github.com/huggingface/transformers/issues/17305
											# We disabled this usage instead as it was outputting wrong outputs.
											lowerCAmelCase   :Union[str, Any]						=			[['HuggingFace is in ', 'Paris is in France']]
											with self.assertRaises(UpperCAmelCase   ):
																	text_classifier(UpperCAmelCase   )

											# This used to be valid for doing text pairs
											# We're keeping it working because of backward compatibility
											lowerCAmelCase   :Optional[int]						=			text_classifier([[['HuggingFace is in ', 'Paris is in France']]]   )
											self.assertEqual(
											    nested_simplify(UpperCAmelCase   )     , [{'label': ANY(UpperCAmelCase   ), 'score': ANY(UpperCAmelCase   )}]     , )
											self.assertTrue(outputs[0]['label'] in model.config.idalabel.values()   )







"""simple docstring"""


import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()


def        UpperCAmelCase      (					a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						lowerCAmelCase   :Tuple						=			MobileBertConfig.from_json_file(a__ )
						print(F"""Building PyTorch model from configuration: {config}""" )
						lowerCAmelCase   :Tuple						=			MobileBertForPreTraining(a__ )
						# Load weights from tf checkpoint
						lowerCAmelCase   :Any						=			load_tf_weights_in_mobilebert(a__     ,      a__     ,      a__ )
						# Save pytorch-model
						print(F"""Save PyTorch model to {pytorch_dump_path}""" )
						torch.save(model.state_dict()     ,      a__ )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE   =   argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '--tf_checkpoint_path', default=None, type=str, required=True, help='Path to the TensorFlow checkpoint path.'
		)
		parser.add_argument(
		    '--mobilebert_config_file',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        'The config json file corresponding to the pre-trained MobileBERT model. \n'
		        'This specifies the model architecture.'
		    ),
		)
		parser.add_argument(
		    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
		)
		__SCREAMING_SNAKE_CASE   =   parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)